Methods and arrangements to adjust communications

ABSTRACT

Logic may adjust communications between customers. Logic may cluster customers into a first group associated with a first subset of synonyms and a second group associated with a second subset of the synonyms. Logic may associate a first tag with the first group and with each of the synonyms of the first subset. Logic may associate a second tag with the second group and with each of the synonyms of the second subset. Logic may associate one or more models with pairs of the groups. A first pair may comprise the first group and the second group. The first model associated with the first pair may adjust words in communications between the first group and the second group, based on the synonyms associated with the first pair, by replacement of words in a communication between customers of the first subset and customers of the second sub set.

RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.16/287,530 entitled “METHODS AND ARRANGEMENTS TO ADJUST COMMUNICATIONS”,filed Feb. 27, 2019, which is a Continuation of U.S. patent applicationSer. No. 16/286,999, entitled “METHODS AND ARRANGEMENTS TO ADJUSTCOMMUNICATIONS” filed on Feb. 27, 2019. The contents of theaforementioned application is incorporated herein by reference.

TECHNICAL FIELD

Embodiments described herein are in the field of text modification. Moreparticularly, the embodiments relate to methods and arrangements toadjust words in communications.

BACKGROUND

Communications between persons with disparate backgrounds is commonplacewhether it be for technical support, customer service, generalcommunications between persons in different geographical locations, orthe like. Such communications can be challenging when persons havesignificantly different educational backgrounds or experiences, ordifferent dialects.

Currently, we rely on the individuals authoring communications topresent ideas in a communication in a form for the intended audience. Insome instances, these audiences are generally known to the personscommunicating. In some instances, these audiences are not known well orjust not known even though information about the recipient(s) may begenerally available.

An author may find that a first attempt at conveyance of an idea useslanguage that is not well understood by the recipient(s). As a result,the author may receive feedback from the recipient(s) of thecommunication indicating that a portion of the communication is not wellunderstood or is misunderstood. In response, the author can revise orotherwise describe the pertinent portions of the communication indifferent terms that may improve the understanding by the recipient(s).Such an iterative process can, in some instances, cause a negativeimpression related to the overall communication, which is neitherhelpful nor conducive to ongoing communications between the author andthe recipient(s).

SUMMARY

Embodiments may include various types of subject matter such as methods,apparatuses, systems, storage media, and/or the like. One embodiment mayinclude a system comprising: memory; and logic circuitry coupled withthe memory. In some embodiments, the logic circuitry may clustercustomers into groups based on customer. The at least two groups maycomprise a first group associated with a first subset of synonyms and asecond group associated with a second subset of the synonyms, whereineach customer is associated with one of the groups. The logic circuitrymay associate a first unique tag with the first group and with each ofthe synonyms of the first subset. The logic circuitry may associate asecond unique tag with the second group and with each of the synonyms ofthe second subset. The first subset and the second subset may compriseunique subsets. The logic circuitry may also associate one or moremodels with pairs of the groups, a first pair of the pairs to comprisethe first group and the second group, a first model of the modelsassociated with the first pair. And the first model may replace at leasta first synonym of the first subset with a second synonym of the secondsubset in a first communication from a first customer of the first groupto a second customer of the second group.

Another embodiment may comprise a non-transitory storage mediumcontaining instructions, which when executed by a processor, cause theprocessor to perform operations. The operations may form a first groupwith a first subset of customers and a second group with a second subsetof the customers; wherein the first subset of the customers and thesecond subset of the customers are mutually exclusive. The operationsmay associate a first unique tag with the first group and with a firstsubset of synonyms. The operations may associate a second unique tagwith the second group and with a second subset of the synonyms. Theoperations may train at least one model to replace synonyms incommunications between the first group and the second group based oncustomer data associated with the first group and the second group. Andthe operations may replace, by the at least one model, at least a firstsynonym from the first subset of synonyms with a second synonym from thesecond subset of synonyms in a first communication from a first customerof the first subset of customers to a second customer of the secondsubset of customers.

Yet another embodiment may comprise a system. The system may comprisememory and logic circuitry coupled with the memory. The logic circuitrymay provide database including a set of synonyms, each of the synonymsassociated with one or more tags, each of the tags associated with oneor more nodes of the database. The logic circuitry may also receive, viaan application programming interface (API), an instruction to replaceone or more words in a document drafted in a text editor, identificationof an author of the document, and identification of a recipient of adocument. The logic circuitry may identify a first tag associated withthe identification of the recipient and a second tag associated with theidentification of the author. The logic circuitry may select a modelbased on an association between the first tag, the second tag, and themodel. The logic circuitry may process, via the API, text of thedocument to identify the one or more words associated with the secondtag to identify one or more candidates for replacement. The logiccircuitry may identify, by the model, synonyms associated with the firsttag to replace the one or more words and replace, via the API, the oneor more words with the synonyms by the model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-C depict embodiments of systems including servers, networks,data servers, and software applications to adjust communications;

FIGS. 1D-E depict embodiments of an apparatus and a neural network of amodel, such as the servers and models in illustrated in FIG. 1A;

FIGS. 1F-G depict embodiments of logic circuitry including a databaseand a data storage to adjust communications with models, such as thelogic circuitry and models in illustrated in FIG. 1A;

FIG. 2 depicts an embodiment of adjust logic circuitry, such as theadjust logic circuitry shown in FIGS. 1A, 1B, 1D, and 1F;

FIGS. 3A-E depict flowcharts of embodiments to adjust communications, byadjust logic circuitry, such as the adjust logic circuitry shown inFIGS. 1A, 1B, 1D, 1F, and 2 ;

FIG. 4 depicts an embodiment of a system including a multiple-processorplatform, a chipset, buses, and accessories such as the server andapparatus shown in FIGS. 1A and 1D; and

FIGS. 5-6 depict embodiments of a storage medium and a computingplatform such as the server(s) shown in FIGS. 1A and 1D.

DETAILED DESCRIPTION OF EMBODIMENTS

The following is a detailed description of embodiments depicted in thedrawings. The detailed description covers all modifications,equivalents, and alternatives falling within the appended claims.

Embodiments discussed herein can generally improve communicationsbetween an author and one or more recipients by application of knowledgeabout the recipient(s) and, in many embodiments, by application ofknowledge about the author. Communications referenced herein refer toany type of textual communications such as text messages, emails, chatsin a chat room or chat box, writings, essays, reports, technical papers,books, e-books, and any other communications that include text. Textrefers to combinations of words in any form such as words characterform, graphical representations of words, emojis, short hand,abbreviations, slang, and/or the like.

Embodiments may adjust communications by replacement of one or morewords in communications between one or more authors and one or morerecipients with synonyms more conducive to correct understanding by therecipient(s). Many embodiments select words in communications to replacewith synonyms based on information known about the recipient(s) of thecommunications. Some embodiments replace words in communications basedon known differences in the backgrounds of the author(s) and therecipient(s). Some embodiments replace words in communications based onassumed differences in the backgrounds of the author(s) and therecipient(s). For instance, some embodiments may assume a high schoollevel or college level of education for recipient(s) for whom theembodiment has no customer data. Further embodiments, may assume adialect of recipient(s) for whom the embodiment has no knowledge or hasminimal customer data about the geographical location of therecipient(s).

For convenience, authors and recipients discussed herein are referred toas customers and information about such customers is referred to ascustomer data. Customer data may refer to communications by a customersuch as writings, papers, chats, texts, and/or the like. Customer datamay also refer to background information such as publicly or privatelyavailable information including, e.g., social media, chat rooms,Internet forums, customer profiles, credit reports, background checks,information presented in web sites, and/or the like.

Some embodiments begin with a simplistic model that clusters customersin groups or otherwise forms groups of customers that identifiesdifferences between the customers such as educational level,geographical location, income level, occupation, and/or the like toidentify likely differences between the customers in terms of vocabularyand/or dialects. For example, a customer in the United States maycommunicate with a customer in the United Kingdom. Dictionaries outlinedifferences in word choices between these customers. In suchcommunications, some embodiments may replace words in communicationsfrom the customer in the United States with synonyms likely moreunderstandable by the customer in the United Kingdom.

Further embodiments may support more granularity based on informationknown about the customer in the United Kingdom and the customer in theUnited States such as dialects typically found in certain regions of theUnited States and dialects found in certain regions of the UnitedKingdom. Such embodiments may replace a word that is a dialect, in thecommunications authored by the customer in the United States, with asynonym that is a dialect in the United Kingdom. Synonyms as discussedherein are any words that are considered by an embodiment to haveequivalent or similar meanings and may be implementation specific and/orcustomizable. For instance, an embodiment may use one or more particulardictionaries to identify synonyms and the embodiments may associate thesynonyms with data structures, tags, and/or the like.

In some embodiments, a set of synonyms may be provided. The set ofsynonyms may include multiple subsets and each of the subsets of thesynonyms may be associated with one of the groups or nodes. Suchembodiments may associate customers with the groups or nodes based oncustomer data associated with each of the customers. Other embodimentsmay cluster the customers into groups and associate subsets of thesynonyms with the groups based on customer data associated with each ofthe groups.

Further embodiments may learn, based on training through naturallanguage processing, words that have equivalent or similar meanings.Some embodiments may learn words that have equivalent or similarmeanings through analyses of customer data associated with customersclustered into the same group. Several embodiments may implement somecombination of synonyms provided and synonyms learned, and such learningmay occur prior to deployment of such embodiments and/or afterdeployment.

Some embodiments may implement natural language processing in models byvectorizing words in customer data and analyzing the customer data to,e.g., cluster customers into groups or nodes based on subsets ofsynonyms associated with or assigned to the groups or nodes. Otherembodiments may associate selected synonyms with the groups or nodes.

As discussed herein, many embodiments associate a unique tag with eachgroup of customers. The unique tag may be any type of link, designation,or other association that can distinguish one group of customers fromanother group of customers. Several embodiments associate the unique tagwith each customer within the group. Furthermore, in some embodiments,an association between a customer and a group is dynamic. In otherwords, embodiments may move customers from one group to another based oncustomer data subsequently collected about the customer such ascommunications authored by the customer and/or updated customer dataobtained from public sources or other sources.

To illustrate, a customer may be known to live in a particular regionthat one embodiment associates with a particular dialect. Incommunications with that customer, the customer may provide feedbackthat is inconsistent with the assumption that the customer understandsthe particular dialect well. For instance, the customer may haverecently relocated into that region from another region associated witha different dialect. Some embodiments track feedback to determine anerror rate associated with replacement of synonyms in communicationswith that customer. After one or more instances of feedback indicate aninconsistency with the assumption that the customer understands theparticular dialect, such embodiments may determine an error rateassociated with the customer that exceeds a threshold for the errorrate. Based on the error rate exceeding the threshold, such embodimentsmay determine that the customer should be associated with a differentgroup of customers and may change the unique tag associated with thecustomer to a different unique tag that associates the customer with adifferent group of customers.

Furthermore, an embodiment may track movement of customers betweengroups. If one customer or multiple customers move between one or moredifferent groups of customers, the embodiment may determine that the onecustomer or multiple customers do not understand well or misunderstandsynonyms in each of the subsets of synonyms associated with existinggroups of customers. In response to such a determination, the embodimentmay determine a need for a new group of customers. Some embodiments mayautomatically generate a new node and train one or more models based onthe customer data associated with the customers being moved to the newnode. Other embodiments may output a report that indicates a need for anew node along with information related to the new node. The informationrelated to the new node may indicate, e.g., the one customer or multiplecustomers and, in some embodiments, a suggested category of synonyms ora specific subset of synonyms that the one customer or multiplecustomers understand based on the feedback.

Many embodiments may associate a unique tag with a subset of synonymsand assign the unique tag to each of the customers in the group toassociate the group of customers with the subset of synonyms. Otherembodiments may associate the unique tag of a group of customers with asubset of synonyms for use in communications associated with that groupof customers.

The subset of synonyms may be one of multiple subsets of synonyms thatform a set of synonyms. In other words, the set of synonyms mayrepresent all the synonyms that an embodiment may use for replacement ofwords in communications between customers. In some embodiments, the setof synonyms may reside in a single database or in each instantiation ofthe single database. In other embodiments, the set of synonyms mayreside in multiple databases as multiple subsets of the set of synonyms.

Furthermore, the subsets of synonyms may overlap. For instance,customers grouped based on education level may include, e.g., adoctorate level group, college level group, a high school level group, amiddle school level group, an elementary level group, one or more groupswith particular technical proficiencies, and/or other groups. Thecustomers in the high school level group and the college level group mayshare an understanding of many of the same synonyms but may also notshare the same understanding of at least some of the synonyms in the setof synonyms. As a result, the high school level group and the collegelevel group may associate with different subsets of synonyms thatinclude some of the same synonyms.

Embodiments may organize and implement adjustments to communications viadifferent methodologies related to the complexity and granularity of theembodiments. The complexity of the embodiments may relate to the numberof groups implemented for the embodiments, the number of customersinvolved, the extent of the customer data known or collected aboutcustomers that is available to the embodiment in a customer dataset,and/or the like. Some embodiments may implement a simple databasestructure that clusters customers into, e.g., two groups, associateseach group with a subset of synonyms, and replaces synonyms incommunications via, e.g., a dictionary or a bag of words.

Other embodiments may implement between 10 and 1000 groups in a morecomplex database structure. The combination of the database structureand the one or more models to adjust communications is referred toherein as adjust logic circuitry. Adjust logic circuitry may comprise acombination of hardware and code such as processor circuitry, hard-codedlogic, instructions executable in the processor circuitry, memory forshort-term and/or long-term storage of instructions and data, and/or thelike.

The adjust logic circuitry may implement a relational database in someembodiments and a graph database in other embodiments. The relationaldatabase may include a set of customers, a set of groups or nodes, asynonym dataset, and, in some embodiments, a set of word vectors. Amodel may comprise an algorithm, a statistical model, or a neuralnetwork. The adjust logic circuitry may identify a word existing in acommunication to replace and the model may select a synonym associatedwith a group of customers to replace the word existing in thecommunication. Tags may associate each of the customers with a group ofcustomers and each group of customers with a subset of synonyms.

The graph database may comprise nodes with edges and each edge of a nodemay interconnect the node with another node via one or more models. Forinstance, in some embodiments, a high school level group may comprise afirst node and a college level group may comprise a second node. Thefirst node may interconnect with the second node through a model thatreplaces words in communications with synonyms for communications fromthe first node to the second node and/or from the second node to thefirst node. In further embodiments, the first node may interconnect withthe second node through two or more models. A first model or set ofmodels may replace words in communications with synonyms forcommunications from the first node to the second node and a second modelor set of models may replace words with synonyms in communications fromthe second node to the first node. For embodiments that implement morethan one model for communications from the first group to the secondgroup, each model may train to replace different categories of synonymssuch as education level related synonyms, dialect related synonyms,and/or the like.

The communications may be text in electronic form or in any form thatcan be converted to electronic form so many embodiments comprise one ormore application programming interface(s) (APIs) that allow an author tointeract with the adjust logic circuitry to replace words incommunications. For instance, the API may be automatically called foreach recipient of a communication or each group of customers associatedwith a communication. In further embodiments, the author may call theadjust logic circuitry via the API by selecting a button associated withthe API in, e.g., a text editor, word processing application,presentation software, and/or any other software application that cancreate and/or transmit a communication from the author to a recipient.

Many embodiments that employ APIs, such as APIs for email programs,create an instance of the communication for each customer groupassociated with the communication. For instance, if an email isaddressed to customers from two different groups of customers, theadjust logic circuitry may create an instance of the email for eachgroup of customers and associate the appropriate email addresses forthose customers with the corresponding instance of the email. For eachinstance of the email, the adjust logic circuitry may execute the modelassociated with the interconnection between the node of the author(s)and the node of the recipient(s). Other embodiments may integrate theadjust logic circuitry with a software application such as an emailprogram, a text editor, a word processor, a presentation softwareapplication, and/or the like.

In some embodiments, the API can automatically capture feedback from arecipient based on natural language processing of one or more emailsfrom one of the recipients of a communication. In other embodiments, theAPI may rely on the author providing feedback to the adjust logiccircuitry.

Several embodiments comprise systems with multiple processor cores suchas central servers, modems, routers, switches, servers, workstations,netbooks, mobile devices (Laptop, Smart Phone, Tablet, and the like),and the like. In various embodiments, these systems relate to specificapplications such as healthcare, home, commercial office and retail,security, industrial automation and monitoring applications, financialservices, and the like.

Turning now to the drawings, FIGS. 1A-C depict embodiments of systemsincluding servers, networks, data servers, and word processingapplications to adjust communications. FIG. 1A illustrates an embodimentof a system 1000. The system 1000 may represent a portion of at leastone wireless or wired network 1020 that interconnects server(s) 1010with data server(s) 1050. The at least one wireless or wired network1020 may represent any type of network or communications medium that caninterconnect the server(s) 1010 and the data server(s) 1050, such as acellular service, a cellular data service, satellite service, otherwireless communication networks, fiber optic services, other land-basedservices, and/or the like, along with supporting equipment such as hubs,routers, switches, amplifiers, and/or the like.

In the present embodiment, the server(s) 1010 may represent one or moreservers owned and/or operated by a company that provides services. Insome embodiments, the server(s) 1010 represent more than one companythat provides services. For example, a first set of one or moreserver(s) 1010 may provide services including identifying a set ofsynonyms for replacement of words in communications for groups ofcustomers including, in some embodiments, indications of educationlevels and/or dialects associated with each of the synonyms. A secondset of one or more server(s) 1010 may associate subsets of the set ofsynonyms with each of the nodes 1016 and 1018 via adjust logic circuitry1015. The adjust logic circuitry 1015 may identify words incommunications from customers associated with node 1016 to customersassociated with node 1018 that are also included in the set of synonymsand in a subset of synonyms associated with the node 1018 and viceversa.

The model(s) 1017 may comprise algorithms or machine learning modelssuch as statistical models, neural networks or other machine learningmodels. In many embodiments, the model(s) 1017 may predict a synonym toreplace a word in a communication, determine a probability of a synonymto replace word in the communication, select a synonym to replace a wordin the communication based on one or more values associated with thesynonym, calculate a value to identify a synonym to replace a word inthe communication, and/or the like. The methodology of the models maydepend on the implementation. The methodology of the model(s) 1017 mayrefer to the model type such as a linear model, a non-linear model,and/or a deep learning model. An example of a linear model may be alogistic regression engine. An example of a non-linear model may be agradient boosting engine. And an example of a deep learning model may bea deep learning neural network.

In the present embodiment, the adjust logic circuitry 1015 comprises asimple implementation of a graph database. The graph database comprisestwo nodes, nodes 1016 and 1018, with a single interconnection betweenthe nodes that is associated with the model(s) 1017. Each node 1016 and1018 may comprise a database including a group of customers. Forinstance, the node 1016 may represent an Information Technology group ofcustomers and the node 1018 may represent customers associated with ahigh school level group. While the customers associated with a highschool level group may have varying levels of education, the InformationTechnology group may adjust communications with their clients via theadjust logic circuitry 1015 to address the broadest possible audience ofclients of the Information Technology group and to improve understandingof communications from their clients.

The group of customers associated with the node 1016 may include eachcustomer representative of the Information Technology group and thegroup of customers associated with the node 1018 may include each of theclients of the Information Technology group. When a client submits arequest for technical support, the adjust logic circuitry 1015 mayidentify the client as a customer associated with the node 1018 andidentify the recipient of the communication as a customer representativeassociated with the node 1016. The adjust logic circuitry 1015 may alsoidentify the model(s) 1017 as the interconnection from an edge of thenode 1018 to an edge of the node 1016. In response to identification ofthe model(s) 1017 as the interconnection from an edge of the node 1018to an edge of the node 1016, the model(s) 1017 may replace wordsincluded the communication authored by the client with synonyms from asubset of synonyms associated with the node 1016. In particular, themodel(s) 1017 may receive, as input, words or word vectors from thecommunication, depending on the model type of the model(s) 1017. In someembodiments, the adjust logic circuitry 1015 may parse the words fromthe text of the communication and search for a matching word in the setof synonyms or in the subset of synonyms associated with the node 1018.The adjust logic circuitry 1015 may provide the words that matchsynonyms in the set of synonyms as input to the model(s) 1017 and themodel(s) 1017 may select replacement synonyms for the words based on thesubset of synonyms associated with the node 1016.

In some embodiments, the model(s) 1017 may associate values withsynonyms in the subset of synonyms associated with the node 1016 basedon feedback from the customer representative. For example, if the subsetof synonyms associated with the node 1016 includes multiple possiblesynonyms that are equivalent to a word in the communication, themodel(s) 1017 may adjust values, such as values between zero and one,for each word replacement that results in negative feedback from thecustomer representative. In one embodiment, the values may reflect or berelated to error rates associated with use of the synonyms as areplacement for a word in the communication and, based on the values,the model(s) 1017 may select between equivalent or similar synonyms inthe subset of synonyms associated with the node 1016.

In other embodiments, the adjust logic circuitry 1015 may identify thewords in the communication that are also included in the set ofsynonyms, convert the text of one or more sentences about each of thewords identified into word vectors, perform a sentence analysis on eachof the one or more sentences, and input the results of the sentenceanalysis along with the word vectors of each of the words identified asinput to the model(s)s 1017. The sentence analysis, for instance, mayinvolve a summation of the word vectors of words in each sentence, oneor more convolutions of the word vectors of words in the one or moresentences, and/or other mathematical processing of the word vectors ofwords in the one or more sentences. The model(s) 1017 may select asynonym for one or more of the words identified from the subset ofsynonyms associated with the node 1016 based on the identified words andthe sentence analysis provided as input to the model(s) 1017.

In still another embodiment, the adjust logic circuitry 1015 mayidentify one or more of the words in the communication that are alsoincluded in the set of synonyms, convert the text of all the sentencesin the communication into word vectors, perform a sentence analysis onthe sentences, perform a communication analysis based on the sentenceanalysis, and input the results of the communication analysis along withthe word vectors of the one or more words identified as input to themodel(s)s 1017. The model(s) 1017 may then select a synonym for one ormore of the words identified from the subset of synonyms associated withthe node 1016 based on the identified words and the communicationanalysis provided as input to the model(s) 1017.

Thereafter, the adjust logic circuitry 1015 may replace the words in thecommunication with the synonyms from the subset of synonyms associatedwith the node 1016 before delivery of the communication to the customerrepresentative of the Information Technology group. In some embodiments,the adjust logic circuitry 1015 may present the altered communication tothe client for approval of the word replacements prior to delivery ofthe communication to the customer representative.

When the customer representative responds to the client, thecommunication from the customer representative may be adjusted in asimilar manner by the model(s) 1017 to replace one or more words withsynonyms from the subset of synonyms associated with the node 1018. Inone embodiment, the model(s) 1017 include one model to replace words ina communication authored by a customer associated with the node 1016 anddirected towards a customer associated with the node 1018, and a secondmodel to replace words in a communication authored by a customerassociated with the node 1018 and directed towards a customer associatedwith the node 1016.

Prior to implementation of the adjust logic circuitry 1015 to adjustcommunications between the customers associated with the nodes 1016 and1018, the adjust logic circuitry 1015 may train the model(s) 1017 withcustomer data for the groups of customers associated with the nodes 1016and 1018. For instance, the adjust logic circuitry 1015 may have accessto a training dataset and a testing dataset for the model(s) 1017 in thecustomer dataset 1054 of the database 1052 on the data server(s) 1050.

To illustrate, the adjust logic circuitry 1015 may retrieve part of orall the customer dataset 1054 to store locally with the server(s) 1010for use as training and testing datasets and designate portions of thecustomer dataset 1054 for training data and portions of the customerdataset 1054 for testing data. In some embodiments, the adjust logiccircuitry 1015 may access the customer dataset 1054 from the dataserver(s) 1050 as needed and may cache some of the customer dataset 1054locally in the server(s) 1010.

In some embodiments, the training dataset and testing dataset mayinclude multiple communications authored by one or more of the customersassociated with the node 1016 and one or more of the customersassociated with the node 1018. In some embodiments, the training datasetand testing dataset may include background information about one or moreof the customers associated with the node 1016 and one or more of thecustomers associated with the node 1018. In further embodiments, thetraining dataset and testing dataset may include background informationabout and communications authored by one or more of the customersassociated with the node 1016 and one or more of the customersassociated with the node 1018. For instance, the model(s) 1017 may trainto associate values with synonyms or for other machine learning based onusage of the synonyms in prior communications by the customers includedin the customer dataset 1054.

FIG. 1B depicts an embodiment for an apparatus 1100 such as one of theserver(s) 1010 shown in FIG. 1A. The apparatus 1100 may be any form ofcomputer and may combine with any suitable embodiment of the systems,devices, and methods disclosed herein. The apparatus 1100 may includeadjust logic circuitry 1110 such as the adjust logic circuitry 1015 inFIG. 1A to replace words in a document 1130 as well as processingcircuitry to execute code associated with a software application 1120and an API 1115. The apparatus 1100 may also include a non-transitorystorage medium to store the document 1130.

The software application 1120 may comprise an application to createand/or edit a communication such as the document 1130. The API 1115 mayprovide an interface between the adjust logic circuitry 1110 and thesoftware application 1120 to facilitate generation of an instance of thedocument 1130 for the adjust logic circuitry 1110. The adjust logiccircuitry 1110 may parse the document into words, identify the words ofthe document that correspond to synonyms in a set of synonyms 1112, andreplace one or more of the words identified with synonyms from a subset1114 of the set of synonyms 1112. In some embodiments. the adjust logiccircuitry 1110 may adjust the document 1130 via the API 1115. In otherembodiments, the adjust logic circuitry 1110 may adjust multipleinstances of the document 1130 to create a version of the document 1130for each of multiple subsets of the synonyms, respectively. Forinstance, a customer may draft the document and select the API 1115 tocreate versions of the document for one or more audiences such as bydesignating one or more attributes of the designated audiences. The oneor more attributes may include an education level, a dialect, anoccupation, a technical proficiency, a geographical location, and/or thelike. In the present embodiment, the user may select one or morecustomers as recipients of the document 1130. The adjust logic circuitry1110 may identify each of the designated recipients as part of one ormore groups of customers. For each group of customers associated withthe one or more recipients, the adjust logic circuitry 1110 may generatean instance of the document 1130 as a communication for the group ofcustomers. In other embodiments, the adjust logic circuitry 1110 maygenerate an instance of the document for each of the one or morerecipients.

FIG. 1C depicts an embodiment of the software application 1120 shown inFIG. 1B. In the present embodiment, the software application 1120creates an instance of the document 1130 referred to as an adjusteddocument 1160 via the adjust logic circuitry 1110 and the API 1115.

After selecting words of the document 1130 to replace, the adjust logiccircuitry 1110 may interact with the software application 1120 to createthe adjusted document 1160 based on the subset of synonyms 1114associated with a recipient of the adjusted document 1160. Inparticular, the adjust logic circuitry 1110 may parse the text of thedocument 1130 to identify words of the text of the document 1130 thatcorrespond to synonyms in the set of synonyms 1112. The words identifiedin the text are designated the synonym 1134, the synonym 1138, and thesynonym 1142. The remainder of the words in the text of the document1130 are designated as the words 1132, 1136, and 1140 and the words1132, 1136, and 1140 may not correspond to synonyms in the set ofsynonyms 1112.

Thereafter, the adjust logic circuitry 1110 may generate the adjusteddocument 1160 by replacing the synonyms 1134 and 1138 with alternatesynonyms 1164 and 1168, respectively. The alternate synonyms 1164 and1168 may be from the subset 1114 of the set of synonyms 1112 that isassociated with the recipient of the adjusted document 1160. If, forinstance, a word, designated as the synonym 1142, from the text of thedocument 1130, that is identified as being in the set of synonyms 1112,does not have a corresponding synonym in the subset 1114, manyembodiments do not replace the word with an alternate synonym.

FIGS. 1D-E depict embodiments of an apparatus and a neural network of amodel, such as the models in illustrated in FIG. 1A. FIG. 1D depicts anembodiment for an apparatus 1100 such as one of the server(s) 1010 shownin FIG. 1A. The apparatus 1200 may be a computer in the form of a smartphone, a tablet, a notebook, a desktop computer, a workstation, or aserver. The apparatus 1200 can combine with any suitable embodiment ofthe systems, devices, and methods disclosed herein. The apparatus 1200can include processor(s) 1210, a non-transitory storage medium 1220,communication interface 1230, and a display device 1235. Theprocessor(s) 1210 may comprise one or more processors, such as aprogrammable processor (e.g., a central processing unit (CPU)). Theprocessor(s) 1210 may comprise processing circuitry to implement adjustlogic circuitry 1215 such as the adjust logic circuitry 1015 in FIG. 1A.

The processor(s) 1210 may operatively couple with a non-transitorystorage medium 1220. The non-transitory storage medium 1220 may storelogic, code, and/or program instructions executable by the processor(s)1210 for performing one or more instructions including the adjust logiccircuitry 1225. The non-transitory storage medium 1220 may comprise oneor more memory units (e.g., removable media or external storage such asa secure digital (SD) card, random-access memory (RAM), a flash drive, ahard drive, and/or the like). The memory units of the non-transitorystorage medium 1220 can store logic, code and/or program instructionsexecutable by the processor(s) 1210 to perform any suitable embodimentof the methods described herein. For example, the processor(s) 1210 mayexecute instructions such as instructions of the adjust logic circuitry1225 causing one or more processors of the processor(s) 1210 representedby the adjust logic circuitry 1215 to adjust a communication. Theprocessor(s) 1210 may adjust the communication via a model such as themodel(s) 1017 of the adjust logic circuitry 1215, based on anassociation of a recipient of the communication with a first subset ofsynonyms such as the subset 1114 shown in FIG. 1B. The adjust logiccircuitry 1215 may analyze the communication to generate a list ofsynonyms in the communication that correspond to synonyms in a set ofsynonyms or a second subset of synonyms that may be associated with theauthor of the communication. The adjust logic circuitry 1215 may replacethe words identified as part of the set of synonyms, or the secondsubset of synonyms, with synonyms from the first subset of synonyms toadjust the communication for the recipient.

Once the recipient receives the communication, the recipient or theauthor may provide feedback to the adjust logic circuitry 1215. Theadjust logic circuitry 1215 may train a model of the adjust logiccircuitry 1215 based on the feedback. In some embodiments, the adjustlogic circuitry 1215 may train the model via negative feedback and/orcalculate an error rate or update an error rate calculation based on thefeedback. In further embodiments, the adjust logic circuitry 1215 maytrain the model with positive feedback or both positive and negativefeedback.

In response to a determination of an error rate associated withreplacement of words with synonyms in the communication, the adjustlogic circuitry 1215 may cause the error rates associated with one ormore customers to output on a display device 1235. In furtherembodiments, the adjust logic circuitry 1215 may cause a report with theerror rates to transmit to another device or server or to a printer viathe communication interface 1230.

The non-transitory storage medium 1220 may store code and data for theadjust logic circuitry 1225 and store a customer dataset 1227 such asthe customer dataset 1054 shown in FIG. 1A. In many embodiments, thecustomer dataset 1227 includes customer data such as communications andbackground information to train one or more models of the adjust logiccircuitry 1215. In some embodiments, memory units of the non-transitorystorage medium 1220 may store data related the customers associated withthe adjust logic circuitry 1215 such as error rates and historicalchanges in group associations of each of the customers.

The processor(s) 1210 may couple to a communication interface 1230 totransmit and/or receive data from one or more external devices (e.g., aterminal, display device, a smart phone, a tablet, a server, a printer,or other remote device). The communication interface 1230 includescircuitry to transmit and receive communications through a wired and/orwireless media such as an Ethernet interface, a wireless fidelity(Wi-Fi) interface, a cellular data interface, and/or the like. In someembodiments, the communication interface 1230 may implement logic suchas code in a baseband processor to interact with a physical layer deviceto transmit and receive wireless communications such as transaction datafrom a server or an instance of a neural network of the adjust logiccircuitry 1215. For example, the communication interface 1230 mayimplement one or more of local area networks (LAN), wide area networks(WAN), infrared, radio, Wi-Fi, point-to-point (P2P) networks,telecommunication networks, cloud communication, and the like.

FIG. 1E depicts an embodiment of a neural network (NN) 1300 of an adjustlogic circuitry, such as the model(s) 1017. The NN 1300 may comprise asa deep neural network (DNN).

A DNN is a class of artificial neural network with a cascade of multiplelayers that use the output from the previous layer as input. An exampleof a DNN is a recurrent neural network (RNN) where connections betweennodes form a directed graph along a sequence. A feedforward neuralnetwork is a neural network in which the output of each layer is theinput of a subsequent layer in the neural network rather than having arecursive loop at each layer.

Another example of a DNN is a convolutional neural network (CNN). A CNNis a class of deep, feed-forward artificial neural networks. A CNN maycomprise of an input layer and an output layer, as well as multiplehidden layers. The hidden layers of a CNN typically consist ofconvolutional layers, pooling layers, fully connected layers, andnormalization layers.

The NN 1300 comprises an input layer 1310, and three or more layers 1320and 1330 through 1340. The input layer 1310 may comprise input data thatis training data for the NN 1300 or at least part of a communication forwhich the NN 1300, in inference mode, will identify synonyms to replacewords of the communication. The input layer 1310 may provide thecustomer data in the form of tensor data to the layer 1320. The tensordata may include a vector, matrix, or the like with values associatedwith each input feature of the NN 1300. In many embodiments, the inputdata may comprise one or more words to replace in the communication. Insome embodiments, the input data may include information about thecommunication or part of the communication along with one or more wordsto replace in the communication.

The customer data may comprise various types of information related to acommunication from a first customer associated with a first group ornode to a second customer associated with a second group or node. Theinformation may include, e.g., historical communications from customersassociated with the first group or node to the customers associated withthe second group or node, background information about customersassociated with the first group or node and customers associated withthe second group or node, and/or the like.

In many embodiments, the input layer 1310 is not modified bybackpropagation. The layer 1320 may compute an output and pass theoutput to the layer 1330. Layer 1330 may determine an output based onthe input from layer 1320 and pass the output to the next layer and soon until the layer 1340 receives the output of the second to last layerin the NN 1300. Depending on the methodology of the NN 1300, each layermay include input functions, activation functions, and/or otherfunctions as well as weights and biases assigned to each of the inputfeatures. The weights and biases may be randomly selected or defined forthe initial state of a new model and may be adjusted through trainingvia backwards propagation (also referred to as backpropagation orbackprop). When retraining a model with customer data obtained after aninitial training of the model, the weights and biases may have valuesrelated to the previous training and may be adjusted through retrainingvia backwards propagation.

The layer 1340 may generate an output, such as a representation of asynonym to replace a word in the communication and/or a probabilityassociated with the synonym, and pass the output to an objectivefunction logic circuitry 1350. The objective function logic circuitry1350 may determine errors in the output from the layer 1340 based on anobjective function such as a comparison of the predicted results againstthe expected results. For instance, the expected results may be pairedwith the input in the training data supplied for the NN 1300 forsupervised training. In one embodiment, for example, the predictedresults may include one or more replacement synonyms for a communicationand the expected results may include synonyms from a subset of synonymsassociated with a particular group of customers identified as therecipient group for this model. In another embodiment, the expectedresults may comprise synonyms found in historical communicationsattributable to the particular group of customers.

During the training mode, the objective function logic circuitry 1350may output errors to backpropagation logic circuitry 1355 tobackpropagate the errors through the NN 1300. For instance, theobjective function logic circuitry 1350 may output the errors in theform of a gradient of the objective function with respect to the inputfeatures of the NN 1300.

The backpropagation logic circuitry 1355 may propagate the gradient ofthe objective function from the top-most layer, layer 1340, to thebottom-most layer, layer 1320 using the chain rule. The chain rule is aformula for computing the derivative of the composition of two or morefunctions. That is, if f and g are functions, then the chain ruleexpresses the derivative of their composition f∘g (the function whichmaps x to f(g(x))) in terms of the derivatives of f and g. After theobjective function logic circuitry 1350 computes the errors,backpropagation logic circuitry 1355 backpropagates the errors. Thebackpropagation is illustrated with the dashed arrows.

When operating in inference mode, the adjust logic circuitry, such asthe adjust logic circuitry 1215 shown in FIG. 1D, may receive feedbackrelated to replacement of words in a communication with synonymsassociated with a group and, in some embodiments, the feedback mayidentify one or more sentences of the communication that include thesynonyms in the form of word vectors or a sentence analysis of the wordvectors for each of the one or more sentences. If the feedback isnegative, the backpropagation may attribute an error to the replacement.If the feedback is positive, the backpropagation may reinforce or biasselection of the replacement within the layers of the NN 1300.

FIGS. 1F-G depict embodiments of adjust logic circuitry 1400 including agraph database 1405 and a data storage 1500 to adjust communicationswith models 1412, 1414, 1416, 1422, 1424, and 1432, such as the model(s)1017 in illustrated in FIG. 1A. The graph database 1405 of adjust logiccircuitry 1400 illustrates an alternative database to the databaseillustrated in FIG. 1A that has more nodes and more interconnections.Note that embodiments are not limited to a particular number of nodesand models but can comprise any number of nodes and models. Thelimitation with respect to the number of nodes and models relates to abalance between the amount of data storage and diminishing returnsassociated with the granularity of attributes associated with thecustomer groups. The attributes may comprise, e.g., different levels ofeducation, different regional dialects, and/or a combination thereof.The granularity of attributes associated with customer dataset 1460 mayalso effectively limit the number of nodes and customer groups.

In many embodiments, the number of nodes is provided and the synonymsassociated with each of the nodes is provided. The adjust logiccircuitry 1400 may receive a set of nodes and each node may beassociated with specified attributes such as one or more levels ofeducation, one or more dialects, and/or the like. In particular, theadjust logic circuitry 1400 may provide a synonyms dataset 1550 withmultiple synonyms 1552 through 1562 and each of the synonyms 1552through 1562 may be designated via tag(s) 1554 through 1564,respectively, to associate each of the synonyms 1552 through 1562 with aparticular regional dialect and/or education level. A subset of synonymsfrom synonym dataset 1550 may be provided for each of the nodes based onthe specified attributes for each of the nodes 1410, 1420, 1430, and1440. Thereafter, the adjust logic circuitry 1400 may cluster thecustomers 1510 with each of the nodes based on the customer dataset 1460by correlating customer data from the customer dataset 1460 against thesubsets of synonyms associated with each of the nodes 1410, 1420, 1430,and 1440. For instance, the adjust logic circuitry 1400 may cluster thecustomers 1510 with each of the nodes by correlating with the synonymdataset 1550, words in prior communications authored by each of thecustomers and/or words associated with the background of each of thecustomers such as level of education, dialect, and/or the like.

In other embodiments, the adjust logic circuitry 1400 may establishattributes associated with each of the nodes 1410, 1420, 1430, and 1440.Such embodiments may implement a cluster model 1450 to separatecustomers into groups based on a predefined number of nodes and commonattributes associated with the customers. In other words, rather thandesigning nodes with specified attributes for each of the nodes, adesigner for the adjust logic circuitry 1400 may provide the synonymsassociated with the attributes, and the adjust logic circuitry 1400 maycluster customers into, e.g., four groups with common sets of theattributes based on the distribution of these attributes amongst thecustomers. The clustering of the customers into four groups mayeffectively assign attributes to the nodes 1410, 1420, 1430, and 1440 byassociating each of the four groups of customers with one of the nodes1410, 1420, 1430, and 1440.

With the synonym dataset 1550 and a customer dataset 1460 such as thecustomer dataset 1054 in FIG. 1A, the cluster model 1450 may cluster thecustomers for, e.g., the four nodes. The cluster model 1450 may clustercustomers into four different groups based on usage of synonyms in thecustomer dataset 1460 and/or background information in the customerdataset 1460 that corresponds to tag(s) associated with the synonyms inthe synonym dataset 1550. In such embodiments, the groups of customersassociated with each of the nodes 1410, 1420, 1430, and 1440 are basedon the distribution of such attributes of amongst the customersrepresented in the customer dataset 1460 rather than based onpre-established attributes specified for each of the nodes. Forinstance, a first group may include college level group of customers inone region, a high school level group of customers in a second region, amixture of college level and high school level group of customers in athird region, and a mixture of high school level and middle school levelgroup of customers in a fourth region. The adjust logic circuitry 1400may establish these four groups based on a probability distribution ofthe customer's attributes that indicates that the existing customersgenerally have attributes within one of these four groups.

In other embodiments, a synonym model 1455 may generate a portion of orall the synonyms in subsets of the synonyms associated with each of thenodes 1410, 1420, 1430, and 1440. For instance, the synonym model 1455may select or identify synonyms for a subset of synonyms associated withthe node 1410 by identifying synonyms in the synonym dataset 1550 thatare also found in communications authored by customers associated withthe node 1410. In such embodiments, the subset of synonyms associatedwith each of the nodes 1410, 1420, 1430, and 1440 may comprise synonymscommonly found in communications authored by the customers associatedwith the nodes 1410, 1420, 1430, and 1440.

Each of the models 1412, 1414, 1416, 1422, 1424, and 1432 may representan interconnection between edges of the nodes 1410, 1420, 1430, and1440. In particular, the model(s) 1412 may establish a functionalinterconnection between the nodes 1410 and 1420 that establishes aprocess, performed by the model(s) 1412, to replace words incommunications between the nodes 1410 and 1420 in a manner discussed inconjunction with FIGS. 1A-1E. Similarly, the model(s) 1414 may establisha functional interconnection between an edge of the node 1410 and anedge of the node 1440. The model(s) 1416 may establish a functionalinterconnection between an edge of the node 1410 and the node 1430. Themodel(s) 1422 may establish a functional interconnection between theedges of the nodes 1420 and 1430. The model(s) 1424 may establish afunctional interconnection between the edges of the nodes 1420 and 1440and the model(s) 1432 may establish a functional interconnection betweenthe edges of the nodes 1430 and 1440.

In other embodiments, the graph database 1405 may not include aninterconnection between each node and every other node in the graphdatabase 1405. For instance, two groups of the customers associated withthe nodes 1410 and 1440 may not communicate with one another and, insuch embodiments, the interconnection with the model(s) 1414 may notexist.

The models 1412, 1414, 1416, 1422, 1424, and 1432 may train based on thesubset of synonyms associated with each of the nodes 1410, 1420, 1430,and 1440 that interconnect the models. For example, the model(s) 1412may interconnect the nodes 1410 and 1420 and may train based on a subsetof synonyms associated with the node 1410 and a subset of synonymsassociated with the node 1420.

In some embodiments, the model(s) 1412 comprise a first model to replacewords in communications from customers associated with the node 1410 tocustomers associated with the node 1420 and a second model to replacewords in communications from customers associated with the node 1420 tocustomers associated with the node 1410. Such embodiments may train thefirst model and the second model to replace words in communicationsbased on the portions of the customer dataset 1460 associated withcustomers associated with the node 1410 and portions of the customerdataset 1460 associated with customers associated with the node 1420.For instance, the first model may train to replace synonyms associatedwith the node 1410 with synonyms associated with the node 1420 and thesecond model may train to replace synonyms associated with the node 1420with synonyms associated with the node 1410. In some embodiments,multiple synonyms from one subset of the synonyms comprise at least partof the input for model(s) 1412. In other embodiments, one synonym fromone subset of the synonyms may comprise at least part of the input forthe models(s) 1412. In still other embodiments, one or more synonyms andanalyses of one or more sentences of a communication may comprise atleast part of the input to the model(s) 1412.

FIG. 1G depicts a data storage 1500 to store data to facilitateadjustment of communications by adjust logic circuitry such as theadjust logic circuitry shown in FIG. 1F. In some embodiments, the datastorage 1500 may represent a local data storage for each node 1410,1420, 1430, and 1440 that includes the subset of customers 1510associated with the node and the subset of synonyms in a synonym dataset1550 associated with each node 1410, 1420, 1430, and 1440. In otherembodiments, the data storage 1500 may represent a central data storagethat maintains a set of customers 1510 for the nodes 1410, 1420, 1430,and 1440, a set of the nodes 1530, a set of synonyms in a synonymdataset 1550, and, in some embodiments, a set of word vectors 1570.

The customers 1510 may include a set of customers 1512 through 1522associated with tags 1514 through 1524, respectively. In someembodiments, the tags 1514 through 1524 may associate each of thecustomers 1512 through 1522 with one of the nodes 1530. In someembodiments, the tags 1514 through 1524 may each include one or morefields to identify an associated node such as a unique number, links toone or more interconnected nodes, links to synonym datasets of linkednodes, links to one or more models that interconnect the node with othernodes, and/or the like. The customers 1512 through 1522 may each includeone or more fields that include information that uniquely identifies acustomer such as a unique number or code, a link to a customer profile,a customer name, an error rate calculated for the customer, a historicalrepresentation of the nodes with which the customer has been associated,and/or the like.

The nodes 1530 may comprise a set of nodes 1532 through 1542 associatedwith tags 1534 through 1544, respectively. In many embodiments, thenodes 1532 through 1542 are associated with mutually exclusive subsets(or groups) of the customers 1510. For instance, the node 1410 may beassociated with a subset of customers 1510 that includes customer 1512.The customer 1512 may only be associated with the node 1410 and may notbe associated with the other nodes 1420, 1430, and 1440. In someembodiments, each of the tags 1514 through tag 1524 may include one ormore fields to identify an associated node such as a unique number, alink to one or more interconnected nodes, a link to synonym datasets oflinked nodes, a link to one or more models that interconnect the nodewith other nodes, and/or the like

The synonym dataset 1550 may include a set of synonyms 1552 through 1562associated with a tag 1554 through a tag 1554, respectively. In someembodiments, the tags 1554 through 1554 may associate each of thesynonyms 1552 through 1562 with one of the nodes 1530 and one or more ofthe customers 1510. Furthermore, each synonym such as the synonym 1552may include a tag in the tag(s) 1554 that associates the synonym 1552with another synonym 1562 to identify the words in the synonym dataset1550 that have equivalent or similar meanings. In many embodiments, thesynonym dataset 1550 includes multiple sets of words and each set ofwords includes one or more synonyms. Note that each of the synonyms 1552through 1562 may be associated with more than one of the nodes 1530 and,in several embodiments, the synonym dataset 1550 does not necessarilyhave a unique synonym associated with each of the nodes 1530. In otherwords, each subset of synonyms associated with a node may be uniquebased on the combination of the synonyms.

Furthermore, each of the tags 1554 through tag 1554 may include one ormore fields to identify an associated node with, e.g., a unique number,to link one or more interconnected nodes, to link to one or more modelsthat interconnect the associated node with other nodes, and/or the like.

The word vectors 1570 may include a set of words 1572 through 1582 alongwith word vectors 1574 through 1584, respectively. The set of words maycomprise words that may be found in communications between customers.The word vectors 1574 through 1584 may include a vectorizedrepresentation of each of the words 1572 through 1582, respectively. Forexample, a word vector may include a set of numbers to uniquely identifyeach word in a set of words. If the set of words includes 100,000 words,a simple, binary representation of each word may include 99,999 zerosand a single one. In other words, in such embodiments, the placement ofthe one in the word vector identifies a specific word. Summing the wordvectors in a sentence can provide a summation that provides attributesof a sentence.

As another example, word vectors 1570 may include vectors of numbersother than one or zero such as the Word2Vec and Naïve Bayes. Thesealternative word vectors offer additional analysis tools for sentenceanalyses. Such sentence analyses may form an input tensor for a machinelearning model such as a neural network to facilitate processing ofsynonym usage in the customer data. The sentence analyses may facilitatean informed selection of synonyms by models to replace words incommunications between the customers 1510. In other embodiments, thecontext of a communication may be represented by inclusion of multiplesynonyms in an input to tensor for the models.

FIG. 2 depicts an embodiment of adjust logic circuitry 2000, such as theadjust logic circuitry shown in FIGS. 1A, 1B, 1D, and 1F. The adjustlogic circuitry 2000 may perform one or more operations to clustercustomers into groups; train a set of models 2012, such as the models1412, 1414, 1416, 1422, 1424, and 1432 illustrated in FIG. 1F; andreplace words in communications between groups of customers to adjustcommunications. The number of models may vary between, e.g., 2 andthousands of models, or may include between, e.g., 50 and 100 models.Note that while the number of models selected for a particularembodiment may affect the granularity with which synonyms can beselected to replace words in communications, the granularity may also bedependent upon the amount of data available in the customer dataset 2005to train models to distinguish synonym usage between customers, amongother factors.

The adjust logic circuitry 2000 may comprise data and logic circuitrysuch as a set of models 2012, a customer cluster 2030, a trainer 2040, adata storage 2050, a monitor model 2060, and a tracker model 2070. Theset of models 2012 may comprise one or more recurrent neural networks,gradient boosting engines, logistic regression engines, statisticalmodels, algorithms, and/or the like, to predict a synonym to replace aword in a communication, which may advantageously attenuatemisunderstanding of the communication by a customer associated with aparticular group of customers. In some embodiments, the set of models2012 may comprise instances of the same model, such as the NN 1300 shownin FIG. 1E.

In some embodiments, the adjust logic circuitry 2000 may be providedwith a set of synonyms in a synonym dataset 2056. The synonym dataset2056 may include multiple synonyms and each of the multiple synonyms maybe associated with one or more unique tags. Each of the one or moreunique tags may identify one of a number (N) different groups or nodes.

With the subsets of synonyms associated with the N groups or nodes, acustomer cluster 2030 may to cluster a set of customers into a number(N) subsets (groups) of customers to associate with the N differentgroups or nodes. For instance, the customer cluster 2030 may processdata from a customer dataset 2051 to generate a probability distributionof the customers associated with the customer dataset 2051 based on theassociation of synonyms with each of the N different groups or nodes.The customer cluster 2030 may then associate customers with the closestof the N different groups or nodes based on the probabilitydistribution. In other words, the customer cluster 2030 may determine aprobability that a customer should be associated with a particular oneof the N different groups or nodes based on a correlation between thecustomer data associated with the customer and the synonyms in thesynonym dataset 2056. As a result, each customer in a subset of thecustomers is associated with one of N different groups or nodes tocreate N mutually exclusive subsets of customers assigned to the Ndifferent groups or nodes. In other words, each subset of customers isassigned to a different one of the N different groups or nodes.

In some embodiments, the adjust logic circuitry 2000 may associate aunique tag to each of the N groups or nodes by storing a unique tag inthe database 2058 in an entry that associates each unique tag with oneof each of the N different groups or nodes to uniquely identify eachgroup or node. The customer cluster 2030 may assign each of thecustomers listed in the database 2058 with one of the N different groupsor nodes by associating the unique tag of one group or node of the Ngroups or nodes with each of the customers in the subset of customersassigned to that one group or node.

In other embodiments, the adjust logic circuitry 2000 may assign theunique tags to each of the mutually exclusive subsets of the customersand associate each of the unique tags to the each of the N groups ornodes to uniquely identify each of the N groups or nodes with the uniquetag.

In further embodiments, the customer cluster 2030 may comprise a wordcomparator 2032. When the customer dataset 2051 includes communicationsauthored by the set of customers in the database 2058, the wordcomparator 2032 may compare words from the communications in thecustomer dataset 2051 with synonyms in a synonym dataset 2056. Thecomparison may identify synonyms associated with each of the customersthat are included in the communications. In some embodiments, the wordcomparator 2032 may also compare the synonyms of each of the customersin a subset of the customers assigned to the same one of the N differentgroups or nodes to detect a common subset of the synonyms for the subsetof customers. In such embodiments, the customer cluster 2030 mayassociate each of the synonyms in the common subset of synonyms with asecond tag to indicate that the synonyms associated with the second tagbelong to a group of synonyms common to communications authored by eachof the customers in a subset of the customers.

The set of models 2012 may include different model methodologiesdepending on the implementation. For instance, the set of models 2012may comprise any type of algorithm or machine learning model such as alinear, non-linear, or deep learning model. In some embodiments, the setof models 2012 may comprise recurrent neural networks such as the neuralnetwork 1300 shown in FIG. 1E.

The set of models 2012 may include models such as the model 2010 tointerconnect the N groups or nodes and to adjust communications betweensubsets of customers associated with the N groups or nodes. Whileoperating in inference mode, the set of models 2012 may receive andmodify a communication prior to transmission or distribution via theAPI(s) 2002. The API(s) may function as an interface with, e.g., a wordprocessing program to edit the communications and store an adjustedcommunication for an author. The set of models 2012 may determine, basedon content of the communication, synonyms to replace words in thecommunication to advantageously adjust the communication for readabilityand/or understandability by a customer that is designated as a recipientfor the communication. For instance, an author of a communication may beassociated with a first subset of customers and a designated recipientfor the communication may be associated with a second subset ofcustomers. The adjust logic circuitry 2000 may identify synonyms in thecommunication based on a comparison of text of the communication with asubset of the synonym dataset 2056 that is associated with the firstsubset of customers. Thereafter, the adjust logic circuitry 2000 mayprovide to the input of a model, such as the model 2012, informationrelated to the synonyms and, in some embodiments, information about thecontext of the communication, the context of sentences in thecommunication about the synonyms, background information about theauthor, and/or the like, to provide the model with input data to selectreplacement synonyms for the communication. The model, which is trainedto adjust communications between the first subset of customers and thesecond subset of customers, may replace synonyms in the communicationbased on prior training to replace synonyms associated with the authorof the communication to synonyms associated with the designatedrecipient of the communication.

In many of the embodiments, a trainer 2040 may train the set of modelswith the customer dataset 2051 residing in the data storage 2050. Thecustomer dataset 2051 may include customer data related to thecommunications authored by and/or background information associated withthe author of the communication and/or customers within the same subsetof customers as the author of the communication.

The adjust logic circuitry 2000 may receive a customer dataset 2005, ora portion thereof, from a database or other data storage; may associateportions of the customer dataset with a training dataset 2052 and atesting dataset 2054; and may store the customer dataset in the datastorage 2050. In some embodiments, the data storage 2050 may cacheportions of the customer dataset 2005 locally.

The trainer 2040 may pretrain new models or retrain existing modelsidentified for inclusion in the set of models 2012. Prior to operationin inference mode, the adjust logic circuitry 2000 may operate themodels in the set of models 2012 such as model 2010 in training mode andtrain the model 2010 with training dataset 2052 from the data storage2050. The model 2010 may switch to inference mode for validation withthe testing dataset 2054 via the validate 2048 logic circuitry todetermine if the model 2010 is trained. For instance, the model 2010 mayadjust communications authored by customers associated with a firstsubset of customers for transmission to a customer associated with asecond subset of customers. The training dataset 2052 may includecustomer data from customers associated with the first subset ofcustomers to process via the model 2010 as well as customer data fromcustomers associated with the second subset of customers to evaluateerror associated with an output of the model 2010. The output of themodel 2010 may include selections of synonyms to replace words fromcommunications authored by the customers associated with the firstsubset of customers. The testing dataset 2054 may comprise customer datafrom the same subsets of customers as the training dataset 2052 so themodel 2010 may be considered trained once the model 2010 can converge onaccurate and/or consistent selections of synonyms to replace words incommunications based on the testing dataset 2054.

The trainer 2040 may repeatedly select sets of customer data from thetraining dataset 2052 for training based on the build sample(s) selectedfor the model 2010. Each set of customer data may include customer datafrom, e.g., a randomly selected customer in the subsets of customersassociated with the model and the sets of customer data may havedifferent counts of synonyms or sizes of communications to,advantageously, increase the robustness of the training.

A backprop 2046 logic circuitry of the trainer 2040 may train the model2010 by backward propagation of the error that is output by the model2010 in response to the training dataset 2052. Backward propagation ofthe error may adjust weights and biases in the layers of the model 2010to reduce the error. The backward propagation of the error mayeffectively adjust the range of predicted synonyms selected in responseto the customer data that caused the model 2010 to output the error.

The data storage 2050 may include a customer dataset 2051 that comprisesthe training dataset 2052 and the testing dataset 2054. The trainingdataset 2052 and the testing dataset 2054 may include customer data fromone or more customers. The data storage 2050 may also include thesynonym dataset 2056, the database 2058, and word vectors 2059. Thesynonym dataset 2056 may include a full set of the synonyms that areidentified in communications and/or full set of the synonyms thatreplace words in communications to adjust communications based oninformation known or assumed about an audience of the communications.

The database 2058 may comprise, e.g. a graph database, a relationaldatabase, or the like, to store a subset or a full set of synonyms, tagsassociated with the synonyms, subsets of customers associated withsubsets of the synonyms via the tags, and nodes associated with tags.The tags may associate the subsets of customers with subsets of thesynonyms and with models in the set of models 2012.

The data storage may also comprise word vectors 2059 as part of thedatabase 2058 or as a distinct data structure such as a table, anotherdatabase, a list, and/or the like. The word vectors 2059 may comprise alist of words that may be included in communications along with valuesassociated with the words in the form of vectors. In other embodiments,the word vectors 2059 may comprise a model such as an algorithm, amathematical model, or the like that can convert words into wordvectors.

After the models in the set of models 2012 are trained with the trainingdataset 2052 and validated with the testing dataset 2054, the set ofmodels 2012 may operate in inference mode to replace words incommunications associated pairs of the nodes or subsets of customers.For instance, each model 2010 of the set of models 2012 may replace aword in a communication authored by a customer associated with a firstnode or first subset of customers with a synonym that is alternativeword with the same meaning or a similar meaning. The alternative wordmay be associated with a customer of a second node or with a customer ina second subset of customers.

The monitor model 2060 may receive feedback 2065 related to thereplacement of words in each communication between nodes or subsets ofcustomers. The monitor model 2060 may receive the feedback 2065 via theAPI(s) 2002 from either a customer that originated a communicationadjusted by one model of the set of models 2012 or a customer that is arecipient of the communication. The feedback 2065 may identify a wordreplaced via the one model and may comprise a positive or negativeindication related to the word replaced. For instances in which thefeedback 2065 comprises a negative feedback, the monitor model 2060 mayrecord the feedback in the form of a value and may calculate an errorrate related to the feedback 2065. In many embodiments, the monitormodel 2060 may relate the feedback 2065 with the one model and mayrelate the feedback 2065 with a subset of customers. In someembodiments, the monitor model 2060 may relate the feedback 2065 with aspecific customer in the subset of customers.

In further embodiments, the feedback 2065 comprises a positive feedbackand the monitor model 2060 may record the feedback in the form of avalue related to the word replaced, the one model, and the customer orsubset of customers associated with the customer that received thecommunication. In many embodiments, the monitor model 2060 may recordthe feedback in the data storage 2050 and/or pass the feedback to thetracker model 2070.

The tracker model 2070 may track and update error rates related to wordreplacements in communications between nodes or subsets of customers andoutput a report including the error rates 2075. In many embodiments, thetracker model 2070 may receive error rates or values related to thefeedback 2065, directly or via the data storage 2050, from the monitormodel 2060 about word replacements in individual communications. Basedon the feedback or values as well as historical values related to thecorresponding subsets of customers and/or the specific customers relatedto the communications, the tracker model 2070 may generate and outputaverage or mean error rates related to corresponding subsets ofcustomers and/or specific customers. For instance, if a customerprovides negative feedback related to one-out-of-three word replacementson average, the tracker model 2070 may track the average and output theaverage error rate or mean error rate associated with each customer. Insome embodiments, the tracker model 2070 may output a historical set oferror rates related to the specific customers and/or the associatedsubsets of customers.

In several embodiments, if the average or mean error rate for a customerexceeds an error rate threshold over a at least a minimum count of wordreplacements, the tracker model 2070 may determine that the customershould be moved to a different subset of customers. The tracker model2070 may automatically associate the customer with a different subset ofcustomers in some embodiments and may output an indication that thecustomer should be moved to a new subset of customers in furtherembodiments.

In some embodiments, the tracker model 2070 may also maintain a historyof movements of customers from one subset of customers to another. Suchembodiments may also determine, after a count of movements based on thenumber of different subsets of customers, that the customer does notcluster well with any of the current subsets of customers. If thetracker model 2070 determines that a customer does not cluster well intoexisting subsets of customers, the tracker model 2070 may, in someembodiments, automatically add a new subset of customers, including thecustomer that did not cluster well into the other subsets of customers.

In one embodiment, when the tracker model 2070 creates a new subset ofcustomers and adds one or more customers to the subset, the customercluster 2030 may generate a subset of synonyms to associate with the newsubset of customers based on analysis or comparison of the customer datafor the one or more customers against the synonym dataset 2056.

FIGS. 3A-E depict flowcharts of embodiments to adjust communications, byadjust logic circuitry, such as the adjust logic circuitry shown inFIGS. 1A, 1B, 1D, 1F, and 2 . FIG. 3A illustrates a flowchart to adjustcommunications between customers. The flowchart starts with clusteringcustomers into groups based on customer data, wherein each customer isassociated with one of the groups (element 3010). Some embodiments maycluster the customers into groups based on the customer data associatedwith the customers. For instance, such embodiments may a statisticalmodel or a machine learning model to associate each of the customerswith one of the groups based on the customer data associated with eachof the customers. The customer data may comprise financial informationabout the customers, geographical information about the customers,historical information about the customers, communications prepared bythe customers, social media associated with the customers, otherinformation associated with the customers, or a combination thereof. Thecommunications may comprise writings authored by the customers and thewritings may include one or more of the synonyms.

In many embodiments, a cluster model may associate customers with afirst group based on an association between the first group and a firstsubset of the synonyms and associate customers with a second group basedon an association between the second group and a second set of synonyms.The cluster model may compare or otherwise correlate synonyms from thesubsets of synonyms with synonyms found in communications authored bythe customers and/or may compare or correlate synonyms in the set ofsynonyms with background information associated each of the customers toassociate the customers with the first group or the second group. Insome embodiments, each of the groups may comprise a node in a graphdatabase and each of the models is associated with an edge of one of thenodes in the graph database.

After clustering the customers into groups, the flowchart may proceed toassociate a unique tag with each of the groups and with subsets ofsynonyms associated with each of the groups. For instance, the adjustlogic circuitry may associate a first unique tag with the first groupand with each of the synonyms of the first subset and associate a secondunique tag with the second group and with each of the synonyms of thesecond subset (element 3015). The subsets of synonyms, while potentiallyhaving multiple common synonyms, may be unique based on inclusion of acombination of synonyms that is different from the other subsets ofsynonyms. An example, the first group of customers may associate with afirst subset of synonyms common to a northwest region of the UnitedStates, the second group of customers may associate with a second subsetof synonyms related to a dialect of the southeast United States, and athird group of customers may associate with a third subset of synonymsrelated to a dialect from the United Kingdom. The first and secondsubsets of synonyms may include many synonyms in common that differ inthe third subset of synonyms.

The flowchart may also associate one or more models with pairs of thegroups (element 3020). The models may comprise machine learning modelssuch as statistical models or neural networks to train based on customerdata associated with the pair of groups of customers to identifysynonyms typically used by a first group of customers that should bereplaced with alternative synonyms to, advantageously improve thechances that customers in a second group of the pair of groups willunderstand a communication authored by a customer in the first group.For instance, the first group of customers may comprise tutors withcollege level or doctorate level educations and the second group ofcustomers may comprise freshman at a college with high school leveleducations. A first model between these two groups may learn words thatare in a synonym dataset that the tutors typically use that are notcommon to the vocabulary of high school level customers so the firstmodel can replace such words with synonyms that are more commonly usedby the customers in the second group. In some embodiments, the firstmodel may also learn, through feedback from customers in the secondgroup, additional words that the tutors typically use that the highschool level customers misunderstand or otherwise do not understandwell.

After associating models with pairs of groups of the customers, thefirst model may replace at least a first synonym of the first subset ofsynonyms with a second synonym of the second subset of synonyms in afirst communication from a first customer of the first group ofcustomers to a second customer of the second group of customers (element3025).

FIG. 3B illustrates a flowchart to adjust communications between groupsof customers. The flowchart begins with clustering the customers intogroups based on the customer data associated with the customers (element3110). In some embodiments, a set of nodes for a graph database areprovided for modelling. A subset of synonyms is associated with eachnode with a unique tag and a cluster model may cluster customers todetermine a probability distribution associated with each of thecustomers that identifies a probability that each customer is associatedwith each of the nodes. The probability distribution may be based on acorrelation between customer data associated with each of the customersand the subsets of synonyms associated with each of the nodes. Forinstance, the subsets of synonyms associated with each of the nodes mayinclude metadata that associates each of the synonyms with an educationlevel, a regional dialect, or a combination thereof and the customerdata may include geographical locations of each of the customers,education levels of each of the customers, synonyms found incommunications authored by each of the customers, and/or the like.

After clustering the customers into groups, the flowchart may trainmodels to replace words in communications between each of the groups(element 3115). The flowchart may train to replace words incommunications authored by a customer of the first group based onsynonyms associated with the first group and synonyms associated withthe second group. The training may involve training data based oncustomer data associated with each of the groups of customers. Thecustomer data may include communications authored by the customers ofthe respective groups and/or may include background information such asthe educational level of the customers in a group, the technicalspecialties of customers in each of the groups, the geographicallocations of each of the customers in the groups, and/or the like.Replacement of words in the communication may, advantageously, tailorcommunications for customers in the second group to improveunderstanding of the content of the communications by customers in thesecond group.

After training the models, the flowchart may select, by a first model, afirst synonym to replace with a second synonym in a communicationauthored by a customer in a first group that designates a customer in asecond group as a recipient (element 3120). In some embodiments, thefirst model may comprise an interconnection between a node associatedwith the first group of customers and a node associated with the secondgroup of customers.

FIG. 3C illustrates a flowchart for tracking error rates of customers.The flowchart may track error rates associated with replacement ofsynonyms in communications between the customers (element 3210). Theerror rates may represent misunderstandings by the customers responsiveto the replacement of synonyms in communications. For instance, atracker model may track feedback from or about customers that receivecommunications. If a customer receives a communication and marks one ormore words as a source of confusion in a communication and the one ormore words include synonyms in a subset of synonyms associated with thatcustomer, the tracker model may determine an error rate associated withthat customer's confusion with words that the customer is expected tounderstand.

FIG. 3D illustrates a flowchart for changing an association of acustomer with a group of customers. In response to determining errorrates associated with customers, the adjust logic circuitry may changeassociations of one or more of customers with the groups based on one ormore of the error rates exceeding a threshold (element 3310). Forinstance, if the error rate associated with a customer exceeds athreshold, the customer is misunderstanding more of the words than thethreshold. In some embodiments, the adjust logic circuitry may respondby changing the association of the customer to a different group that ismore compatible with the customer's vocabulary and/or dialect. Toillustrate, the adjust logic circuitry may cluster a first customer witha first group of customers based on the geographical location of thecustomers in the first group. The intention for the grouping may be toreplace synonyms related to differences in the dialect of the firstgroup of customers from other groups of customers. The first customer,however, may have recently moved to that geographical location from adifferent geographical location and may have a dialect that is morecompatible with a second group of customers. In such situations, theadjust logic circuitry may calculate an error rate for the firstcustomer based on misunderstandings by the first customer that are basedon the dialect associated with the first group of customers. When theerror rate of the first customer exceeds the threshold, the adjust logiccircuitry may determine that the error rate for the first customerindicates that the first customer should be associated with a differentgroup of customers and may change the association of the first customerfrom the first group of customers to the second group of customers.

FIG. 3E illustrates a flowchart to adjust communications. The flowchartbegins with providing a database including a set of synonyms, each ofthe synonyms associated with one or more tags, each of the tagsassociated with one or more nodes of the database (element 3410). Inother words, the database may comprise a set of nodes and each of thenodes may represent an education level and/or a dialect. The set ofsynonyms may include synonyms associated, via tags, with each of thenodes for identifying words typically known by customers associated withthe particular nodes.

Adjust logic circuitry associated with the database may receive, via anapplication programming interface (API), an instruction to replace oneor more words in a document drafted in the text editor, identificationof an author of the document, and identification of a recipient of adocument (element 3415). In response, the adjust logic circuitry mayidentify a first tag associated with the identification of the recipientand a second tag associated with the identification of the author(element 3420). Thereafter, the adjust logic circuitry may select amodel based on the association between the first tag, the second tag,and the model (element 3425). In other words, the adjust logic circuitrymay access the database to identify a model that is associated with therecipient of the document and the author of the document.

After selecting the model, the adjust logic circuitry may process, viathe API, text of the document to identify the one or more wordsassociated with the second tag (element 3430) to identify, e.g., wordsin the set of synonyms associated with the author. The model mayidentify synonyms associated with the first tag to replace the one ormore words (element 3435) and replace, via the API, the one or morewords with the synonyms by the model (element 3440). In other words, themodel may identify words that the author typically uses in the documentthat may, advantageously be replaced with synonyms that the recipienttypically uses and replace those words with the synonyms.

FIG. 4 illustrates an embodiment of a system 4000 such as a server ofthe server(s) 1010 shown in FIG. 1A or the apparatus 1100 shown in FIG.1B. The system 4000 is a computer system with multiple processor coressuch as a distributed computing system, supercomputer, high-performancecomputing system, computing cluster, mainframe computer, mini-computer,client-server system, personal computer (PC), workstation, server,portable computer, laptop computer, tablet computer, handheld devicesuch as a personal digital assistant (PDA), or other device forprocessing, displaying, or transmitting information. Similar embodimentsmay comprise, e.g., entertainment devices such as a portable musicplayer or a portable video player, a smart phone or other cellularphone, a telephone, a digital video camera, a digital still camera, anexternal storage device, or the like. Further embodiments implementlarger scale server configurations. In other embodiments, the system4000 may have a single processor with one core or more than oneprocessor. Note that the term “processor” refers to a processor with asingle core or a processor package with multiple processor cores.

As shown in FIG. 4 , system 4000 comprises a motherboard 4005 formounting platform components. The motherboard 4005 is a point-to-pointinterconnect platform that includes a first processor 4010 and a secondprocessor 4030 coupled via a point-to-point interconnect 4056 such as anUltra Path Interconnect (UPI). In other embodiments, the system 4000 maybe of another bus architecture, such as a multi-drop bus. Furthermore,each of processors 4010 and 4030 may be processor packages with multipleprocessor cores including processor core(s) 4020 and 4040, respectively.While the system 4000 is an example of a two-socket (2S) platform, otherembodiments may include more than two sockets or one socket. Forexample, some embodiments may include a four-socket (4S) platform or aneight-socket (8S) platform. Each socket is a mount for a processor andmay have a socket identifier. Note that the term platform refers to themotherboard with certain components mounted such as the processors 4010and the chipset 4060. Some platforms may include additional componentsand some platforms may only include sockets to mount the processorsand/or the chipset.

The first processor 4010 includes an integrated memory controller (IMC)4014 and point-to-point (P-P) interconnects 4018 and 4052. Similarly,the second processor 4030 includes an IMC 4034 and P-P interconnects4038 and 4054. The IMC's 4014 and 4034 couple the processors 4010 and4030, respectively, to respective memories, a memory 4012 and a memory4032. The memories 4012 and 4032 may be portions of the main memory(e.g., a dynamic random-access memory (DRAM)) for the platform such asdouble data rate type 3 (DDR3) or type 4 (DDR4) synchronous DRAM(SDRAM). In the present embodiment, the memories 4012 and 4032 locallyattach to the respective processors 4010 and 4030. In other embodiments,the main memory may couple with the processors via a bus and sharedmemory hub.

The processors 4010 and 4030 comprise caches coupled with each of theprocessor core(s) 4020 and 4040, respectively. In the presentembodiment, the processor core(s) 4020 of the processor 4010 include anadjust logic circuitry 4026 such as the adjust logic circuitry 1110shown in FIG. 1B. The adjust logic circuitry 4026 may representcircuitry configured to replace words in a communication with synonymsassociated with recipients of the communication within the processorcore(s) 4020 or may represent a combination of the circuitry within aprocessor and a medium to store all or part of the functionality of theadjust logic circuitry 4026 in memory such as cache, the memory 4012,buffers, registers, and/or the like. In several embodiments, thefunctionality of the adjust logic circuitry 4026 resides in whole or inpart as code in a memory such as the adjust logic circuitry 4096 in thedata storage unit 4088 attached to the processor 4010 via a chipset 4060such as the adjust logic circuitry 1110 shown in FIG. 1B. Thefunctionality of the adjust logic circuitry 4026 may also reside inwhole or in part in memory such as the memory 4012 and/or a cache of theprocessor. Furthermore, the functionality of the adjust logic circuitry4026 may also reside in whole or in part as circuitry within theprocessor 4010 and may perform operations, e.g., within registers orbuffers such as the registers 4016 within the processor 4010, registers4036 within the processor 4030, or within an instruction pipeline of theprocessor 4010 or the processor 4030.

In other embodiments, more than one of the processor 4010 and 4030 maycomprise functionality of the adjust logic circuitry 4026 such as theprocessor 4030 and/or the processor within the deep learning accelerator4067 coupled with the chipset 4060 via an interface (I/F) 4066. The I/F4066 may be, for example, a Peripheral Component Interconnect-enhanced(PCI-e).

The first processor 4010 couples to a chipset 4060 via P-P interconnects4052 and 4062 and the second processor 4030 couples to a chipset 4060via P-P interconnects 4054 and 4064. Direct Media Interfaces (DMIs) 4057and 4058 may couple the P-P interconnects 4052 and 4062 and the P-Pinterconnects 4054 and 4064, respectively. The DMI may be a high-speedinterconnect that facilitates, e.g., eight Giga Transfers per second(GT/s) such as DMI 3.0. In other embodiments, the processors 4010 and4030 may interconnect via a bus.

The chipset 4060 may comprise a controller hub such as a platformcontroller hub (PCH). The chipset 4060 may include a system clock toperform clocking functions and include interfaces for an I/O bus such asa universal serial bus (USB), peripheral component interconnects (PCIs),serial peripheral interconnects (SPIs), integrated interconnects (I2Cs),and the like, to facilitate connection of peripheral devices on theplatform. In other embodiments, the chipset 4060 may comprise more thanone controller hub such as a chipset with a memory controller hub, agraphics controller hub, and an input/output (I/O) controller hub.

In the present embodiment, the chipset 4060 couples with a trustedplatform module (TPM) 4072 and the unified extensible firmware interface(UEFI), BIOS, Flash component 4074 via an interface (I/F) 4070. The TPM4072 is a dedicated microcontroller designed to secure hardware byintegrating cryptographic keys into devices. The UEFI, BIOS, Flashcomponent 4074 may provide pre-boot code.

Furthermore, chipset 4060 includes an I/F 4066 to couple chipset 4060with a high-performance graphics engine, graphics card 4065. In otherembodiments, the system 4000 may include a flexible display interface(FDI) between the processors 4010 and 4030 and the chipset 4060. The FDIinterconnects a graphics processor core in a processor with the chipset4060.

Various I/O devices 4092 couple to the bus 4081, along with a bus bridge4080 which couples the bus 4081 to a second bus 4091 and an I/F 4068that connects the bus 4081 with the chipset 4060. In one embodiment, thesecond bus 4091 may be a low pin count (LPC) bus. Various devices maycouple to the second bus 4091 including, for example, a keyboard 4082, amouse 4084, communication devices 4086 and a data storage unit 4088 thatmay store code such as the adjust logic circuitry 4096. Furthermore, anaudio I/O 4090 may couple to second bus 4091. Many of the I/O devices4092, communication devices 4086, and the data storage unit 4088 mayreside on the motherboard 4005 while the keyboard 4082 and the mouse4084 may be add-on peripherals. In other embodiments, some or all theI/O devices 4092, communication devices 4086, and the data storage unit4088 are add-on peripherals and do not reside on the motherboard 4005.

FIG. 5 illustrates an example of a storage medium 5000 to storeprocessor data structures. Storage medium 5000 may comprise an articleof manufacture. In some examples, storage medium 5000 may include anynon-transitory computer readable medium or machine readable medium, suchas an optical, magnetic or semiconductor storage. Storage medium 5000may store various types of computer executable instructions, such asinstructions to implement logic flows and/or techniques describedherein. Examples of a computer readable or machine-readable storagemedium may include any tangible media capable of storing electronicdata, including volatile memory or non-volatile memory, removable ornon-removable memory, erasable or non-erasable memory, writeable orre-writeable memory, and so forth. Examples of computer executableinstructions may include any suitable type of code, such as source code,compiled code, interpreted code, executable code, static code, dynamiccode, object-oriented code, visual code, and the like. The examples arenot limited in this context.

FIG. 6 illustrates an example computing platform 6000. In some examples,as shown in FIG. 6 , computing platform 6000 may include a processingcomponent 6010, other platform components or a communications interface6030. According to some examples, computing platform 6000 may beimplemented in a computing device such as a server in a system such as adata center or server farm that supports a manager or controller formanaging configurable computing resources as mentioned above.Furthermore, the communications interface 6030 may comprise a wake-upradio (WUR) and may be capable of waking up a main radio of thecomputing platform 6000.

According to some examples, processing component 6010 may executeprocessing operations or logic for apparatus 6015 described herein suchas the adjust logic circuitry 1015 and 1110 illustrated in FIGS. 1A and1B, respectively. Processing component 6010 may include various hardwareelements, software elements, or a combination of both. Examples ofhardware elements may include devices, logic devices, components,processors, microprocessors, circuits, processor circuits, circuitelements (e.g., transistors, resistors, capacitors, inductors, and soforth), integrated circuits, application specific integrated circuits(ASIC), programmable logic devices (PLD), digital signal processors(DSP), field programmable gate array (FPGA), memory units, logic gates,registers, semiconductor device, chips, microchips, chip sets, and soforth. Examples of software elements, which may reside in the storagemedium 6020, may include software components, programs, applications,computer programs, application programs, device drivers, systemprograms, software development programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof. Determining whether an example isimplemented using hardware elements and/or software elements may vary inaccordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints, as desired for a given example.

In some examples, other platform components 6025 may include commoncomputing elements, such as one or more processors, multi-coreprocessors, co-processors, memory units, chipsets, controllers,peripherals, interfaces, oscillators, timing devices, video cards, audiocards, multimedia input/output (I/O) components (e.g., digitaldisplays), power supplies, and so forth. Examples of memory units mayinclude without limitation various types of computer readable andmachine readable storage media in the form of one or more higher speedmemory units, such as read-only memory (ROM), random-access memory(RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronousDRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasableprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), flash memory, polymer memory such as ferroelectric polymermemory, ovonic memory, phase change or ferroelectric memory,silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or opticalcards, an array of devices such as Redundant Array of Independent Disks(RAID) drives, solid state memory devices (e.g., USB memory), solidstate drives (SSD) and any other type of storage media suitable forstoring information.

In some examples, communications interface 6030 may include logic and/orfeatures to support a communication interface. For these examples,communications interface 6030 may include one or more communicationinterfaces that operate according to various communication protocols orstandards to communicate over direct or network communication links.Direct communications may occur via use of communication protocols orstandards described in one or more industry standards (includingprogenies and variants) such as those associated with the PCI Expressspecification. Network communications may occur via use of communicationprotocols or standards such as those described in one or more Ethernetstandards promulgated by the Institute of Electrical and ElectronicsEngineers (IEEE). For example, one such Ethernet standard may includeIEEE 802.3-2012, Carrier sense Multiple access with Collision Detection(CSMA/CD) Access Method and Physical Layer Specifications, Published inDecember 2012 (hereinafter “IEEE 802.3”). Network communication may alsooccur according to one or more OpenFlow specifications such as theOpenFlow Hardware Abstraction API Specification. Network communicationsmay also occur according to Infiniband Architecture Specification,Volume 1, Release 1.3, published in March 2015 (“the InfinibandArchitecture specification”).

Computing platform 6000 may be part of a computing device that may be,for example, a server, a server array or server farm, a web server, anetwork server, an Internet server, a work station, a mini-computer, amain frame computer, a supercomputer, a network appliance, a webappliance, a distributed computing system, multiprocessor systems,processor-based systems, or combination thereof. Accordingly, functionsand/or specific configurations of computing platform 6000 describedherein, may be included or omitted in various embodiments of computingplatform 6000, as suitably desired.

The components and features of computing platform 6000 may beimplemented using any combination of discrete circuitry, ASICs, logicgates and/or single chip architectures. Further, the features ofcomputing platform 6000 may be implemented using microcontrollers,programmable logic arrays and/or microprocessors or any combination ofthe foregoing where suitably appropriate. It is noted that hardware,firmware and/or software elements may be collectively or individuallyreferred to herein as “logic”.

It should be appreciated that the computing platform 6000 shown in theblock diagram of FIG. 6 may represent one functionally descriptiveexample of many potential implementations. Accordingly, division,omission or inclusion of block functions depicted in the accompanyingfigures does not infer that the hardware components, circuits, softwareand/or elements for implementing these functions would necessarily bedivided, omitted, or included in embodiments.

One or more aspects of at least one example may be implemented byrepresentative instructions stored on at least one machine-readablemedium which represents various logic within the processor, which whenread by a machine, computing device or system causes the machine,computing device or system to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores”, may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

Various examples may be implemented using hardware elements, softwareelements, or a combination of both. In some examples, hardware elementsmay include devices, components, processors, microprocessors, circuits,circuit elements (e.g., transistors, resistors, capacitors, inductors,and so forth), integrated circuits, application specific integratedcircuits (ASIC), programmable logic devices (PLD), digital signalprocessors (DSP), field programmable gate array (FPGA), memory units,logic gates, registers, semiconductor device, chips, microchips, chipsets, and so forth. In some examples, software elements may includesoftware components, programs, applications, computer programs,application programs, system programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof. Determining whether an example isimplemented using hardware elements and/or software elements may vary inaccordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints, as desired for a givenimplementation.

Some examples may include an article of manufacture or at least onecomputer-readable medium. A computer-readable medium may include anon-transitory storage medium to store logic. In some examples, thenon-transitory storage medium may include one or more types ofcomputer-readable storage media capable of storing electronic data,including volatile memory or non-volatile memory, removable ornon-removable memory, erasable or non-erasable memory, writeable orre-writeable memory, and so forth. In some examples, the logic mayinclude various software elements, such as software components,programs, applications, computer programs, application programs, systemprograms, machine programs, operating system software, middleware,firmware, software modules, routines, subroutines, functions, methods,procedures, software interfaces, API, instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof.

According to some examples, a computer-readable medium may include anon-transitory storage medium to store or maintain instructions thatwhen executed by a machine, computing device or system, cause themachine, computing device or system to perform methods and/or operationsin accordance with the described examples. The instructions may includeany suitable type of code, such as source code, compiled code,interpreted code, executable code, static code, dynamic code, and thelike. The instructions may be implemented according to a predefinedcomputer language, manner or syntax, for instructing a machine,computing device or system to perform a certain function. Theinstructions may be implemented using any suitable high-level,low-level, object-oriented, visual, compiled and/or interpretedprogramming language.

Some examples may be described using the expression “in one example” or“an example” along with their derivatives. These terms mean that aparticular feature, structure, or characteristic described in connectionwith the example is included in at least one example. The appearances ofthe phrase “in one example” in various places in the specification arenot necessarily all referring to the same example.

Some examples may be described using the expression “coupled” and“connected” along with their derivatives. These terms are notnecessarily intended as synonyms for each other. For example,descriptions using the terms “connected” and/or “coupled” may indicatethat two or more elements are in direct physical or electrical contactwith each other. The term “coupled,” however, may also mean that two ormore elements are not in direct contact with each other, but yet stillco-operate or interact with each other.

In addition, in the foregoing Detailed Description, it can be seen thatvarious features are grouped together in a single example for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the claimed examplesrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive subject matter lies in lessthan all features of a single disclosed example. Thus, the followingclaims are hereby incorporated into the Detailed Description, with eachclaim standing on its own as a separate example. In the appended claims,the terms “including” and “in which” are used as the plain-Englishequivalents of the respective terms “comprising” and “wherein,”respectively. Moreover, the terms “first,” “second,” “third,” and soforth, are used merely as labels, and are not intended to imposenumerical requirements on their objects.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code to reduce the number of times code must be retrievedfrom bulk storage during execution. The term “code” covers a broad rangeof software components and constructs, including applications, drivers,processes, routines, methods, modules, firmware, microcode, andsubprograms. Thus, the term “code” may be used to refer to anycollection of instructions which, when executed by a processing system,perform a desired operation or operations.

Logic circuitry, devices, and interfaces herein described may performfunctions implemented in hardware and also implemented with codeexecuted on one or more processors. Logic circuitry refers to thehardware or the hardware and code that implements one or more logicalfunctions. Circuitry is hardware and may refer to one or more circuits.Each circuit may perform a particular function. A circuit of thecircuitry may comprise discrete electrical components interconnectedwith one or more conductors, an integrated circuit, a chip package, achip set, memory, or the like. Integrated circuits include circuitscreated on a substrate such as a silicon wafer and may comprisecomponents. And integrated circuits, processor packages, chip packages,and chipsets may comprise one or more processors.

Processors may receive signals such as instructions and/or data at theinput(s) and process the signals to generate the at least one output.While executing code, the code changes the physical states andcharacteristics of transistors that make up a processor pipeline. Thephysical states of the transistors translate into logical bits of onesand zeros stored in registers within the processor. The processor cantransfer the physical states of the transistors into registers andtransfer the physical states of the transistors to another storagemedium.

A processor may comprise circuits to perform one or more sub-functionsimplemented to perform the overall function of the processor. Oneexample of a processor is a state machine or an application-specificintegrated circuit (ASIC) that includes at least one input and at leastone output. A state machine may manipulate the at least one input togenerate the at least one output by performing a predetermined series ofserial and/or parallel manipulations or transformations on the at leastone input.

The logic as described above may be part of the design for an integratedcircuit chip. The chip design is created in a graphical computerprogramming language and stored in a computer storage medium or datastorage medium (such as a disk, tape, physical hard drive, or virtualhard drive such as in a storage access network). If the designer doesnot fabricate chips or the photolithographic masks used to fabricatechips, the designer transmits the resulting design by physical means(e.g., by providing a copy of the storage medium storing the design) orelectronically (e.g., through the Internet) to such entities, directlyor indirectly. The stored design is then converted into the appropriateformat (e.g., GDSII) for the fabrication.

The resulting integrated circuit chips can be distributed by thefabricator in raw wafer form (that is, as a single wafer that hasmultiple unpackaged chips), as a bare die, or in a packaged form. In thelatter case, the chip is mounted in a single chip package (such as aplastic carrier, with leads that are affixed to a motherboard or otherhigher-level carrier) or in a multichip package (such as a ceramiccarrier that has either or both surface interconnections or buriedinterconnections). In any case, the chip is then integrated with otherchips, discrete circuit elements, and/or other signal processing devicesas part of either (a) an intermediate product, such as a processorboard, a server platform, or a motherboard, or (b) an end product.

What is claimed is:
 1. An apparatus comprising: memory; and logiccircuitry coupled with the memory to: receive a document comprisingtext; determine to replace one or more words in the document with one ormore synonyms; identify a first tag associated with a recipient and asecond tag associated with an author; select a model based on anassociation between the first tag, the second tag, and the model;process, with the model, the text of the document to identify the one ormore words based on an association between the one or more words and thesecond tag; identify, by the model, the one or more synonyms based on anassociation between the one or more synonyms and the first tag; andreplace the one or more words with the one or more synonyms in thedocument.
 2. The apparatus of claim 1, wherein the document is generatedby the author and intended for the recipient, and the logic circuitry todetermine to replace the one or more words based on known differences,assumed differences, or both between the author and the recipient. 3.The apparatus of claim 2, wherein a known difference is a location ofthe author having a first dialect and a location of the recipient havinga second dialect.
 4. The apparatus of claim 2, wherein a knowndifference is a language of the author having a first dialect and alanguage of the recipient having a second dialect.
 5. The apparatus ofclaim 1, wherein the first tag identifies a subset of synonyms and isassigned to a first group including the recipient.
 6. The apparatus ofclaim 1, wherein the second tag identifies a second subset of synonymsand is assigned to a second group including the author.
 7. The apparatusof claim 1, comprising storage coupled with the memory and the logiccircuitry, the storage configured to store models and a set of nodes fora graph database, each node to represent an education level, a dialect,or both, and wherein the first tag is associated with a first node, andthe second tag is associated with a second node.
 8. The apparatus ofclaim 7, the logic circuitry configured to determine the selected modelbased on a linking in the graph database between the first node, thesecond node, and the selected model.
 9. A computer-implemented methodcomprising: receiving a document comprising text; receiving anindication to replace one or more words in the document with one or moresynonyms; identifying a first tag associated with a recipient and asecond tag associated with an author; selecting a model based on anassociation between the first tag, the second tag, and the model;processing, with the model, the text of the document to identify the oneor more words based on an association between the one or more words andthe second tag; identifying, by the model, the one or more synonymsbased on an association between the one or more synonyms and the firsttag; and replacing the one or more words with the one or more synonymsin the document.
 10. The computer-implemented method of claim 9, whereinthe document is generated by the author and intended for the recipient,and the method comprising determining to replace the one or more wordsbased on known differences, assumed differences, or both between theauthor and the recipient.
 11. The computer-implemented method of claim10, wherein a known difference is a location of the author having afirst dialect and a location of the recipient having a second dialect.12. The computer-implemented method of claim 10, wherein a knowndifference is a language of the author having a first dialect and alanguage of the recipient having a second dialect.
 13. Thecomputer-implemented method of claim 9, wherein the first tag identifiesa subset of synonyms and is assigned to a first group including therecipient.
 14. The computer-implemented method of claim 9, wherein thesecond tag identifies a second subset of synonyms and is assigned to asecond group including the author.
 15. The method of claim 1, comprisingstoring, in a storage, models and a set of nodes for a graph database,each node to represent an education level, a dialect, or both, andwherein the first tag is associated with a first node, and the secondtag is associated with a second node.
 16. The computer-implementedmethod of claim 15, comprising determining the selected model on alinking in the graph database between the first node, the second node,and the selected model.
 17. A computer-implemented method comprising:receiving a document comprising text, a first identifier to identify anauthor, and a second identifier to identify a recipient; identifying,via the second identifier, a first tag associated with the recipient;identifying, via the first identifier, a second tag associated with theauthor; selecting a model based on a linking between the first tag, thesecond tag, and the model in a graph database; processing, with themodel, the text of the document to identify the one or more words basedon an association between the one or more words and the second tag;identifying, by the model, the one or more synonyms based on anassociation between the one or more synonyms and the first tag; andreplacing the one or more words with the one or more synonyms in thedocument.
 18. The computer-implemented method of claim 17, wherein thefirst tag identifies a subset of synonyms and is assigned to a firstgroup including the recipient.
 19. The computer-implemented method ofclaim 17, wherein the second tag identifies a second subset of synonymsand is assigned to a second group including the author.
 20. Thecomputer-implemented method of claim 17, comprising storing in a storagemodels and a set of nodes for a graph database, each node to representan education level, a dialect, or both, and wherein the first tag isassociated with a first node, and the second tag is associated with asecond node.